Modeling linkage disequilibrium increases accuracy of polygenic risk scores

Vilhjálmsson, Bjarni J., Yang, Jian, Finucane, Hilary K., Gusev, Alexander, Lindström, Sara, Ripke, Stephan, Genovese, Giulio, Loh, Po-Ru, Bhatia, Gaurav, Do, Ron, Hayeck, Tristan, Won, Hong-Hee, Kathiresan, Sekar, Pato, Michele, Pato, Carlos, Tamimi, Rulla, Stahl, Eli, Zaitlen, Noah, Pasaniuc, Bogdan, Belbin, Gillian, Kenny, Eimear E., Schierup, Mikkel H., De Jager, Philip, Patsopoulos, Nikolaos A., McCarroll, Steve, Daly, Mark, Purcell, Shaun, Chasman, Daniel, Neale, Benjamin, Goddard, Michael, Visscher, Peter M., Kraft, Peter, Patterson, Nick, Price, Alkes L., Holmans, Peter Alan ORCID: https://orcid.org/0000-0003-0870-9412, Escott-Price, Valentina ORCID: https://orcid.org/0000-0003-1784-5483, Hamshere, Marian L. ORCID: https://orcid.org/0000-0002-8990-0958 and O'Donovan, Michael Conlon ORCID: https://orcid.org/0000-0001-7073-2379 2015. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. American Journal of Human Genetics 97 (4) , pp. 576-592. 10.1016/j.ajhg.2015.09.001

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Abstract

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine Advanced Research Computing @ Cardiff (ARCCA) MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Subjects:	R Medicine > R Medicine (General)
Additional Information:	Peter Holmans, Valentina Escott-Price, Marian Hamshere and Michael O'Donovan are collaborators on this article.
Publisher:	Elsevier (Cell Press)
ISSN:	0002-9297
Date of Acceptance:	1 September 2015
Last Modified:	31 Oct 2022 10:40
URI:	https://orca.cardiff.ac.uk/id/eprint/85637

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